Beyond Species Detection: eDNA Metabarcoding and Metagenomic Skimming Workflows for Population-level Genetic Inference in the Common Sole (Solea solea)

Genetic diversity is now formally recognised as a core component of biodiversity, yet most fisheries monitoring provides no genetic information, and conventional trawl surveys face growing spatial, environmental and ethical constraints. Environmental DNA (eDNA) offers a complementary, non-invasive alternative, but its use in marine fisheries remains largely limited to species detection. Using the common sole (Solea solea), a commercially important but under-monitored flatfish, this thesis evaluated whether COI metabarcoding and metagenomic skimming can support population-level genetic inference from samples collected on commercial and survey trawls. A metabarcoding workflow refined for intraspecific detection recovered 28 inferred ASVs and supported population-level analysis, which revealed an almost completely shared haplotype pool and no significant differentiation between the Celtic Sea/Irish Sea, and southern North Sea populations, consistent with the weak population structure previously reported for S. Solea in the region. Metagenomic skimming reliably detected the species, but the low and variable proportion of target reads left nuclear coverage too shallow to recover intraspecific variation, and reliable genetic signal was confined largely to the mitochondrial genome. Both approaches require further development before achieving management-relevant quantitative performance, although some population-level signal can already be recovered, particularly with metabarcoding.